Deterministisches Edge-Computing für Bedside-SaMD & kritische Biosignal-Inferenz

SaMDCompute führt komplexe physiologische Inferenz direkt am Point-of-Care aus und eliminiert Cloud-Abhängigkeiten sowie Latenz-Jitter für kritische Entscheidungen bei Arrhythmien und Atemstillständen.

SaMDCompute Bedside Edge Architecture

Deterministic Processing Enclave and Biosignal Accelerator

Central System: Clinical Edge Inference Engine - Deterministic RTOS Core

Clinical IoMT Nodes

Quantized Neural Engine
RTOS Memory Enclave
Bedside Telemetry Ingress
Hazard Supervisor
EHR Export Bridge

Architectural Layers & Regulatory Standards

Deterministic Microkernel Layer (seL4 Microkernel / FreeRTOS MPU Medical Profile): Formally verified microkernel enforcing priority preemption and bounded interrupt latency.
Memory Protection & Isolation Enclave (ARM TrustZone-M / RISC-V PMP (Physical Memory Protection)): Hardware-enforced MPU boundaries preventing non-critical logging tasks from corrupting vital algorithms.
Hardware-Accelerated Neural Inference (CMSIS-NN Vector Kernels / INT8 Quantized Tensor Arrays): Low-power vector NPU executing quantized 1D/2D CNNs for real-time electrophysiological wave analysis.
Clinical Decision & Dispatch Interface (IEC 60601-1-8 Audio/Visual Hazard Controller): Synchronous dispatch of high-priority alarms to bedside displays and central nurse consoles.

Key Metrics: < 100 µs (Worst-Case Interrupt Latency) | 100% (Formal Memory Safety Guarantee) | INT8 (Quantized Waveform Inference) | Class C (IEC 62304 Software Architecture)

Vorhersagbares Low-Latency-Computing, wo Sekunden Leben retten

Cloud-based medical algorithms are vulnerable to internet outages and packet jitter. SaMDCompute standardizes edge acceleration platforms that guarantee worst-case execution times for real-time hemodynamic alerting and automated drug delivery.

Core Engineering Areas

RTOS in Class C Medical Devices — Hard real-time deterministic scheduling algorithms preventing priority inversion in ventilators and monitors.
Hardware-Enforced Memory Isolation — Isolating untrusted third-party drivers from core patient safety algorithms.
Deterministic Deep Learning Inference at Bedside — Achieving predictable latency and zero memory fragmentation in medical AI inference.

Technical Articles

Related Cluster Architecture Specifications

HealthVigilance — Zero-Trust IoMT Architecture, SaMD Risk Engineering & Autonomous Clinical Post-Market Vigilance
AgenticSaMD — Autonomous Agentic Software as a Medical Device, Multi-Agent Clinical Decision Support & FDA GMLP Guardrails
AITelemonitoring — Autonomous AI Telemonitoring, Virtual Nursing Assistants & Predictive Clinical Deterioration Scoring
BCI2IoMT — Brain-Computer Interface IoMT Gateways, Neural Decoding & Electrophysiological Streaming